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Computer Science

D-Index
34
Citations
7082
World Ranking
11980
National Ranking
103

Overview

Robert Legenstein is a researcher affiliated with Graz University of Technology in Austria. Their work spans multiple disciplines, focusing primarily on engineering, neuroscience, and computer science. Within these fields, Legenstein's contributions address a range of specialized subfields including electrical and electronic engineering, cognitive neuroscience, artificial intelligence, cellular and molecular neuroscience, and computer vision and pattern recognition.

The main topics of Legenstein's research include advanced memory and neural computing, neural dynamics and brain function, neural networks and reservoir computing, ferroelectric and negative capacitance devices, neural networks and applications, neuroscience and neural engineering, and neuroscience and neuropharmacology research.

Legenstein has published extensively, with frequent appearances in venues such as arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Zenodo (CERN European Organization for Nuclear Research), Nature Communications, and PLoS Computational Biology.

  • A solution to the learning dilemma for recurrent networks of spiking neurons, 2020, Nature Communications
  • Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Spike frequency adaptation supports network computations on temporally dispersed information, 2021, eLife
  • Dendritic Computing: Branching Deeper into Machine Learning, 2021, Neuroscience
  • Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring, 2020, Frontiers in Computational Neuroscience

Their research collaboration network includes frequent co-authors such as Ozan Özdenizci, Wolfgang Maass, Thomas Limbacher, Maximilian Baronig, and Michael G. Müller, reflecting interdisciplinary cooperation across neural computation and related areas.

Best Publications

  • Integration of nanoscale memristor synapses in neuromorphic computing architectures

    Giacomo Indiveri;Bernabé Linares-Barranco;Robert A. Legenstein;George Deligeorgis

  • 2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models

    Robert Legenstein;Wolfgang Maass

  • A solution to the learning dilemma for recurrent networks of spiking neurons

    Guillaume Emmanuel Fernand Bellec;Franz Scherr;Anand Subramoney;Elias Hajek

  • Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models

    Unknown

  • Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses.

    Alexander Serb;Johannes Bill;Ali Khiat;Radu Berdan

  • Combining predictions for accurate recommender systems

    Michael Jahrer;Andreas Töscher;Robert Legenstein

  • A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

    Robert A. Legenstein;Dejan Pecevski;Wolfgang Maass

  • What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?

    Robert Legenstein;Christian Naeger;Wolfgang Maass

  • Long short-term memory and Learning-to-learn in networks of spiking neurons

    Guillaume Emmanuel Fernand Bellec;Darjan Salaj;Anand Subramoney;Robert Legenstein

  • Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons

    Lars Büsing;Benjamin Schrauwen;Robert Legenstein

  • Deep Rewiring: Training very sparse deep networks

    Guillaume Bellec;David Kappel;Wolfgang Maass;Robert Legenstein

  • Branch-Specific Plasticity Enables Self-Organization of Nonlinear Computation in Single Neurons

    Robert Legenstein;Wolfgang Maass

  • Emergence of Complex Computational Structures From Chaotic Neural Networks Through Reward-Modulated Hebbian Learning

    Gregor M. Hoerzer;Robert Legenstein;Wolfgang Maass

  • Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system

    Sebastian Schmitt;Johann Klahn;Guillaume Bellec;Andreas Grubl

  • Network Plasticity as Bayesian Inference.

    David Kappel;Stefan Habenschuss;Robert A. Legenstein;Wolfgang Maass

  • A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task

    Robert Legenstein;Steven M. Chase;Andrew B. Schwartz;Wolfgang Maass

  • What makes a dynamical system computationally powerful

    Robert Albin Legenstein;Wolfgang Maass

  • Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

    Sebastian Schmitt;Johann Klaehn;Guillaume Bellec;Andreas Gruebl

  • A compound memristive synapse model for statistical learning through STDP in spiking neural networks.

    Johannes Bill;Robert Legenstein

  • Reinforcement learning on slow features of high-dimensional input streams.

    Robert A. Legenstein;Niko Wilbert;Laurenz Wiskott

  • Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets.

    Guillaume Bellec;Franz Scherr;Elias Hajek;Darjan Salaj

Frequent Co-Authors

Wolfgang Maass
Wolfgang Maass Graz University of Technology
Christos H. Papadimitriou
Christos H. Papadimitriou Columbia University
Santosh Vempala
Santosh Vempala Georgia Institute of Technology
Laurenz Wiskott
Laurenz Wiskott Ruhr University Bochum
Themistoklis Prodromakis
Themistoklis Prodromakis University of Southampton
Andrew B. Schwartz
Andrew B. Schwartz University of Pittsburgh
Steve Furber
Steve Furber University of Manchester
P. Jesper Sjöström
P. Jesper Sjöström McGill University
Bernabé Linares-Barranco
Bernabé Linares-Barranco University of Seville
Giacomo Indiveri
Giacomo Indiveri University of Zurich

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